US10879708B2 - Battery management system - Google Patents
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- US10879708B2 US10879708B2 US15/653,640 US201715653640A US10879708B2 US 10879708 B2 US10879708 B2 US 10879708B2 US 201715653640 A US201715653640 A US 201715653640A US 10879708 B2 US10879708 B2 US 10879708B2
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Definitions
- This disclosure relates to predicting a battery management system performance based on a time series analysis and a neural network of historical data of a battery system associated with the battery management system.
- a battery management system is an electronic system that manages a rechargeable battery system that includes one or more cells or battery packs.
- the BMS may protect the battery from operating outside a safe operating region and monitor a state of the battery (e.g., voltage, temperature, current, etc.).
- the BMS may calculate data associated with the battery and report the calculated data to external devices for monitoring, controlling the environment of the battery system, and authenticating and/or balancing the battery system.
- a battery built together with a BMS having an external communication bus becomes a smart battery.
- the rechargeable battery system may be configured to store electrical energy on a large scale within an electrical power grid. For example, electrical energy is stored during times when production (for example, from intermittent power plants, such as renewable electricity sources, such as wind power, tidal power, solar power) exceeds consumption, and is returned to the grid when production falls below consumption. As such, the rechargeable battery system stores electrical energy when grid consumption is low and uses the stored electrical energy at times when consumption exceeds production from the power plant.
- One aspect of the disclosure provides a method implemented on data processing hardware that includes receiving current measurements from at least one current sensor configured to measure current of a battery system in communication with a power distribution network having a power plant distributing power to one or more consumers.
- the method also includes receiving voltage measurements from at least one voltage sensor configured to measure voltage of the battery system and temperature measurements from at least one temperature sensor configured to measure temperature of the battery system.
- the method includes determining an impedance parameter of the battery system based on the received measurements, a temperature parameter of the battery system based on the received measurements, a predicted voltage parameter based on the impedance parameter, and a predicted temperature parameter based on the temperature parameter.
- the method includes commanding the battery system to charge power from the power plant or discharge power from the power plant based on the predicted voltage parameter and the predicted temperature parameter.
- the transfer function H(w) may be defined as a ratio between a Fourier transform of an output variable y(t) and an input variable x(t), where the output variable y(t) is one of the impedance parameter or the temperature parameter, and the input variable x(t) is one or more of the received measurements.
- the transfer function H(w) in a discrete domain may be determined as:
- the method includes updating an impedance profile, a voltage profile, or a temperature profile based on the voltage measurements or the temperature measurements.
- determining the predicted voltage parameter includes training the data processing hardware to generate a best fit of the voltage measurements or the temperature measurements. In some implementations, predicting, by the data processing hardware, the predicted voltage parameter or the predicted temperature parameter based on the best fit of the voltage measurements or the temperature measurements, respectively.
- the memory hardware may be in communication with the data processing hardware and may store instructions that when executed on the data processing hardware cause the data processing hardware to perform operations.
- the operations include receiving current measurements from at least one current sensor configured to measure current of a battery system in communication with a power distribution network having a power plant distributing power to one or more consumers.
- the operations also include receiving voltage measurements from at least one voltage sensor configured to measure voltage of the battery system.
- the operations further include receiving temperature measurements from at least one temperature sensor configured to measure temperature of the battery system.
- the operations also include determining an impedance parameter of the battery system based on the received measurements, and determining a temperature parameter of the battery system based on the received measurements.
- determining the impedance parameter or the temperature parameter comprises determining a transfer function H(w) of a time series ⁇ (t) defined in a time interval [ ⁇ T,T], where T is an integer greater than zero.
- the transfer function H(w) may be defined as a ratio between a Fourier transform of an output variable y(t) and an input variable x(t), where the output variable y(t) is one of the impedance parameter or the temperature parameter, and the input variable x(t) is one or more of the received measurements.
- the transfer function H(w) in a discrete domain may be determined as:
- determining one of the predicted voltage parameter or the predicted temperature parameter includes executing a time series analysis implementing an auto-regressive model.
- the method may also include implementing a neural network approach, an empirical recursive method, or a Yule-Walker approach to determine an optimal solution of the auto-regressive model AR(p).
- commanding the battery system to charge power from the power plant includes commanding the battery system to store power from the power plant.
- the operations include updating an impedance profile, a voltage profile, or a temperature profile based on the voltage measurements or the temperature measurements.
- determining the predicted voltage parameter includes training the data processing hardware to generate a best fit of the voltage measurements or the temperature measurements.
- the operations include predicting the predicted voltage parameter or the predicted temperature parameter based on the best fit of the voltage measurements or the temperature measurements, respectively.
- the operations include tracking a remaining available capacity of the battery system and determining one of a charge state or life cycle of the battery system.
- determining the impedance parameter or the temperature parameter includes determining a transfer function H(w) of a time series ⁇ (t) defined in a time interval [ ⁇ T, T], where T is an integer greater than zero.
- the transfer function H(w) may be defined as a ratio between a Fourier transform of an output variable y(t) and an input variable x(t), where the output variable y(t) is one of the impedance parameter or the temperature parameter, and the input variable x(t) is one or more of the received measurements.
- the transfer function H(w) in a discrete domain may be determined as:
- determining one of the predicted voltage parameter or the predicted temperature parameter includes executing a time series analysis implementing an auto-regressive model.
- commanding the battery system to charge power from the power plant includes commanding the battery system to store power from the power plant.
- the operations include updating an impedance profile, a voltage profile, or a temperature profile based on the voltage measurements or the temperature measurements.
- determining the predicted voltage parameter includes training the data processing hardware to generate a best fit of the voltage measurements or the temperature measurements. Determining the predicted voltage parameter may also include predicting, by the data processing hardware, the predicted voltage parameter or the predicted temperature parameter based on the best fit of the voltage measurements or the temperature measurements, respectively.
- the instructions when executed on the data processing hardware, cause the data processing hardware to track a remaining available capacity of the battery system and determine one of a charge state or life cycle of the battery system.
- FIG. 1 is a functional block diagram of an exemplary power distribution network.
- FIG. 2A is a functional block diagram of an exemplary controller of the power distribution network of FIG. 1 .
- FIG. 2B is a schematic view of an exemplary arrangement of operations for a method of predicting a performance of an energy storage system.
- FIG. 2C is a functional block diagram of an exemplary controller of the power distribution network of FIG. 1 .
- FIG. 3 is a functional block diagram of an exemplary artificial neural network.
- FIG. 4 is a graph of an exemplary input current obtained from battery system sensors.
- FIG. 5 is a graph of an exemplary voltage predicted from training and testing the neural network of FIG. 3 .
- FIG. 6A is a graph of an exemplary temperature predicted from training and testing the neural network of FIG. 3 .
- FIG. 6B is a graph of an exemplary remaining capacity predicted from training and testing the neural network of FIG. 3 .
- FIG. 6C is a detailed view of a portion of the graph of FIG. 6B .
- FIG. 7 is a graph of an exemplary input current obtained from battery system sensors.
- FIG. 8 is a graph of an exemplary voltage predicted using a Yule-Walker approach.
- FIG. 9 is a graph of an exemplary temperature predicted using a Yule-Walker approach.
- FIG. 10 is a functional block diagram of an exemplary heat exchange between a battery system and its immediate external environment.
- FIG. 11 is a graph of an exemplary calculation of the maximum and minimum temperature associated with a charging cycle.
- FIG. 12 is a graph of an exemplary prediction of the heat capacity and heat associated with a charging cycle.
- FIG. 13A is a graph of an exemplary current frequency regulation profile associated with a charging cycle.
- FIG. 13B is a graph of an exemplary voltage frequency regulation profile associated with a charging cycle.
- FIG. 14 is a graph of an exemplary comparison between predicted and measured data associated with a charging cycle.
- FIG. 15A is a graph of an exemplary power profile.
- FIG. 15B is a graph of exemplary measured and predicted voltages using power training data of the power profile shown in FIG. 15A .
- FIG. 16 is a graph of an exemplary power and its associated state-of-charge.
- FIG. 17 is a graph of an exemplary voltage profile and a minimum cut-off voltage.
- FIG. 18 provides a schematic view of an exemplary arrangement of operations for a method of predicting future battery system parameters.
- FIG. 19 is a schematic view of an exemplary computing device executing any systems or methods described herein.
- FIG. 1 illustrates an exemplary power distribution network 100 configured to transmit power from a power plant 110 to an energy storage system 120 and to individual consumers 130 .
- the energy storage system 120 includes a battery management system (BMS) 122 in communication with a rechargeable battery system 124 .
- the battery system 124 includes power storage devices (e.g., batteries) configured to capture power from the power plant 110 and store the power for distribution at a later time.
- the rechargeable battery system 124 stores electrical energy on a large scale within the power distribution network 100 .
- the behavior of the ensemble of batteries is the same as the average behavior of the individual batteries.
- the cost to supply electricity varies during the course of a single day.
- the wholesale price of electricity on the power distribution network 100 reflects the real-time cost for supplying electricity from the power plant 110 .
- electricity demand is usually highest in the afternoon and early evening (peak hours), and costs to provide electricity are usually higher at these times.
- most consumers 130 pay prices based on the seasonal average cost of providing electricity. Therefore, the consumers 130 do not notice the daily fluctuation in electricity prices.
- the energy storage system 120 stores renewable energy, such as energy produced by wind and solar, which are intermittent, and therefore the rechargeable battery system 124 stores the intermittent renewable energy to provide smooth electricity to the consumers 130 .
- the consumers 130 may include one or more of a house consumer 130 a , a factory consumer 130 b , a business consumer 130 c , or any other consumer that receives electrical power from the power distribution network 100 .
- the BMS 122 manages the rechargeable battery system 124 and protects the battery from operating outside a safe operating state.
- the BMS 122 monitors the performance of the battery system 124 , for example, by monitoring a voltage, a temperature, and a current of the battery system 124 . Consequently, the BMS 122 may report the monitored data to a controller 200 .
- the BMS 122 performs calculations on the monitored data before sending the data to the controller 200 ; while in other examples, the BMS 122 sends the controller 200 the raw data.
- the energy storage system 120 may be in communication with the controller 200 via a network 10 .
- the network 10 may include various types of networks, such as a local area network (LAN), wide area network (WAN), and/or the Internet.
- the controller 200 receives data from the BMS 122 , e.g., from sensors 123 associated with the battery system 124 of the energy storage system 120 , and monitors the sensors 123 to predict the performance of the battery system 124 . As such, the controller 200 executes a series analysis of the received data 126 from the sensors 123 .
- the initial cycles of received data 126 from the sensors 123 are considered as learning cycles or learning parameters, which are used to predict an impedance/resistance profile (at impedance transfer function 242 ) that is applied to predict the voltage (at voltage prediction 252 b ) and temperature (at temperature prediction 254 b ). Since the controller 200 continuously receives the data 126 from the sensors 123 , for example, via the network 10 , the controller 200 continuously updates the profile of the resistance and therefore the profiles of the voltage and temperatures. As such, the controller 200 may execute algorithms to predict the performance of the battery system 124 and use the predicted metrics to bid on the electrical power of the BMS into the market.
- the learning cycles or learning parameters utilized by the algorithms described herein allow the controller 200 to predict future performance of the battery system 124 to avoid failures thereof and to accurately and efficient instruct the BMS 122 to transmit energy to or from the battery system 124 for storage or use.
- the algorithms allow the BMS 122 to predict the future performance of the battery system 124 and make decisions (e.g., instruct the battery system 124 to store electrical power or discharge electrical power) to increase the efficiency and longevity of the energy storage system 120 .
- Utilization of the learning cycles by the algorithms allows the BMS 122 to learn from previously generated data 126 transmitted from the sensors 123 in order to build accurate models that predict future performance of the battery system 124 .
- the predictions and decisions made by the BMS 122 increase the efficiency and longevity of the battery system 124 relative to battery management systems relying on rigid, static rules. For example, modeling and optimization based on the previously generated data 126 increases the accuracy of quantified measurements of the data 126 , while adjusting decisions and outcomes in response to changes in the data 126 ensures accurate measurements and interpretation of the data 126 .
- the utilization of robust programming protocols or procedures reduces the amount of time required for the controller 200 to execute the algorithms described herein.
- the controller 200 includes memory hardware 210 that stores instructions and data processing hardware 220 that executes the instructions to perform one or more operations.
- the memory hardware 210 may be physical devices used to store programs (e.g., sequences of instructions) or data (e.g., program state information) on a temporary or permanent basis for use by a computing device.
- the non-transitory memory may be volatile and/or non-volatile addressable semiconductor memory. Examples of non-volatile memory include, but are not limited to, flash memory and read-only memory (ROM)/programmable read-only memory (PROM)/erasable programmable read-only memory (EPROM)/electronically erasable programmable read-only memory (EEPROM) (e.g., typically used for firmware, such as boot programs). Examples of volatile memory include, but are not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), phase change memory (PCM) as well as disks or tapes.
- RAM random access memory
- the data processing hardware 220 may be in communication with the memory hardware 210 and may execute the instructions to perform one or more operations. In some implementations, the data processing hardware 220 executes an initial cycle extraction function 230 , a transfer function calculation function 240 , and a prediction function 250 . The executed functions 230 , 240 , 250 use the captured sensor data 126 to predict the future performance of the energy storage system 120 , specifically the BMS 122 .
- the BMS 122 includes a current sensor 123 a , a voltage sensor 123 b , and a temperature sensor 123 c associated with the battery system 124 .
- the data processing hardware 220 receives sensor signals 126 a , 126 b , 126 c from each one of the current sensor 123 a , the voltage sensor 123 b , and the temperature sensor 123 c , respectively.
- the initial cycle extraction function 230 , the transfer function calculation function 240 , and the prediction function 250 executed on the data processing hardware 220 may evolve and update as more signal data 126 is received from the BMS 122 .
- the core of the functions 230 , 240 , 250 are based on an evolving training process that includes the use of historical signal data 126 recorded by the BMS 122 from the sensors 123 in the time interval [t, t+n] to predict the data at cycle t+n+1, where t is a current time and n is positive number greater than zero.
- the data processing hardware 220 executes a cost function that defines a mathematical relationship between the different data signal 126 associated with each one of the sensors 123 a , 123 b , 123 c . Accordingly, the training data is continuously evolving since the selection of the historical signal data 126 is revisited every n cycles. This continuous update of the historical signal data 126 allows the controller 200 to capture changes in the signal data 126 during its cycling regimen. The optimization of a training factor may depend on the characteristics of the BMS under-consideration.
- the controller 200 receives input variables, i.e., sensor signals 126 a , 126 b , 126 c , and outputs prediction output variables 270 a , 270 b using training parameters 128 a , 128 b , 128 c outputted from the training data selection function 230 .
- inputs-outputs relationship can be defined as transfer functions (i.e., the impedance transfer function 242 and a temperature transfer function 244 ) of the controller 200 .
- the transfer function H(w), i.e., the impedance transfer function 242 and/or the temperature transfer function 244 , executed by the controller 200 is a ratio between the Fourier transform of the output variable y(t) and the input variable x(t) and is defined in the discrete domain as:
- the transfer function is defined as
- the transfer function is defined as:
- the controller 200 uses the outputs of the transfer function H(w) (e.g., transfer function calculation function 240 ) to train a neural network 300 ( FIG. 3 ) and to test for predictions.
- the transfer function H(w) is defined in the Fourier domain as a complex vector, having two parts: a magnitude
- the inverse Fourier transform function may be applied to the product between the trained transfer function and the current Fourier transform.
- the magnitude of the current Fourier transform defines a voltage magnitude and a temperature magnitude that is obtained by converting the complex values into real values for further analysis and visualization.
- phase ⁇ of the transfer function H(w) is shown by:
- and the phase ⁇ of the transfer function H(w) may vary over time and frequency.
- is used to quantify the performance metrics and to track and monitor the voltage and temperature distribution over time for maintenance purposes.
- the phase ⁇ is used to show the angular differences between the measured and predicted data.
- the phase ⁇ may also be used as a metric to track phase changes in the data that could be interpreted in some situations as deviations from a normal stage.
- the transfer function H(w) is applied to two separate transfer functions, which are the voltage/current 270 a (impedance function 242 ) and the temperature/current 270 b (temperature function 244 ).
- the transfer function H(w) (e.g., transfer function calculation function 240 ) outputs training parameters 128 , i.e., impedance training parameters 128 b / 128 a and temperature training parameters 128 c / 128 a.
- the prediction function 250 uses current sensor data 126 a to predict the voltage and the temperature (predicted voltage 272 a , predicted temperature 272 b ), with the previous recorded values of the voltage and the temperature 270 a , 270 b used in the training data selection 230 .
- the prediction function 250 includes a training step 252 and a testing step 254 .
- the prediction function 250 is applied to each one of the voltage and temperature separately.
- the prediction function 250 includes the steps of matching training impedance data 252 a , matching training temperature data 254 a , predict voltage 252 b , and predict temperature 254 b.
- the controller 200 is trained on the historical training data 126 to generate the best fit of the topology of the training data 126 and then uses those training entities to predict BMS data 272 into the future.
- any learning machine needs representative examples of the data 128 in order to capture the underlying structure that allows it to generalize to new or predicted cases.
- the controller 200 may be considered as an adaptive filtering, since it depends only on the intrinsic structure of the data 128 .
- the training step 252 a , 254 a includes cross-validating the predicted BMS data 272 relative to the historical training data 126 to minimize convergence errors.
- the training step 252 a , 254 a is optimized by combining, with the controller 200 , for example, the inputs (e.g., current signal 126 a , voltage signal 126 b , or temperature signal 126 c ) from each of the sensors (e.g., the current sensor 123 a , the voltage sensor 123 b , or the temperature sensor 123 c , respectively) in order to reduce the quantity of learning cycles.
- the training step 252 a , 254 a may use only the first five learning cycles as inputs to converge into a minimal convergence error.
- Table (1) shows a mean square error (MSE) between the measured data (e.g., current signal 126 a , voltage signal 126 b , or temperature signal 126 c ) and the predicted data (e.g., predicted BMS data 272 ), where the error is minimized during the training step.
- MSE mean square error
- the controller 200 predicts new data 272 using the overall structure captured during the training step 252 a , 254 a , i.e., the best fit of the topology of the training data 126 .
- the testing step 252 b , 254 b is similar to the training step 252 a , 254 a .
- the current sensor data 126 a is used as the input.
- the voltage prediction function 252 includes the training step 252 a and the testing step 252 b .
- the controller 200 applies the voltage prediction function 252 to both the voltage data 126 b and the temperature data 126 c .
- the current signal 126 a is considered as a known variable of the voltage prediction function 252 .
- the current signal 126 a may be determined from a power of the battery system 124 using Ohm's law.
- the controller 200 determines the resistance/impedance prior to the application of Ohm's law.
- the temperature prediction function 254 includes the training step 254 a and the testing step 254 b .
- Heat is an important parameter that affects the health of the battery system 124 of the energy storage system 120 . Therefore, it is important to track the temperature of the battery system 124 over time.
- the controller 200 determines the prediction function 250 by using time series analysis.
- a time series is a sequence of data points drawn from successively equally spaced points in time, i.e., sensor data 126 a , 126 b , 126 c from the sensors 123 . Therefore, the time series is a sequence of discrete-time data.
- Time series analysis includes a method for analyzing the time series data to extract meaningful statistics and other characteristics of the time series data.
- the controller 200 employs an auto-regressive (AR) model on the time series.
- An AR model is a representation of a type of random process, as such, it describes certain time-varying processes in nature, economics, etc.
- the AR model specifies that the output variable depends linearly on its own previous values and on stochastic term (an imperfectly predictable term). Thus, the model is in the form of a stochastic difference equation.
- the notation AR(p) indicates an autoregressive model of order p.
- the controller 200 determines an optimal solution of the AR model time series predictions by executing one of a neural network approach, an empirical recursive method, or a Yule-Walker Approach.
- ANNs Artificial neural networks
- the ANNs are a family of models inspired by biological neural networks, such as the central nervous system of animals, and, in particular, the brain.
- the ANNs are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown.
- the ANNs are defined using three components: architecture rule; activity rule; and learning rule.
- the architecture rule of the ANNs specifies variables that are involved in the network and their topological relationships.
- the activity rule defines how the activities of neurons change in response to each other.
- the learning rule specifies the way in which the neural network's rights change with time (see FIG. 3 ).
- the weight vector w is commonly ordered first by layer, then by neurons, and finally by the weights of each neuron plus its bias.
- the controller 200 uses the Levenberg-Marquardt algorithm to solve the nonlinear system of the neural network 300 .
- the Levenberg-Marquardt algorithm (LMA) is also known as the damped least-squares (DLS) method, and is used to solve non-linear least square problems.
- the Jacobian matrix is approximated using the chain rule and the first derivatives of the activation functions.
- the chains rule is a formula for computing the derivative of the composition of two or more functions. For example, if f and g are functions, then the chain rule expresses the derivative of their composition.
- Back propagation neural network is a supervised learning algorithm in which the input data are supplied together with the desired output.
- the BPN has two hidden layers.
- the BPN learns during a training epoch. In this case, the BPN goes through 1000 epochs with momentum of 0.5 and learning rate 0.5 to converge to the optimal solution in which the error is minimized.
- a training epoch for each entry consists of the following steps: feed input data into the network; initialize weights; check output against desired value and feedback error; calculate the error; and update the weights between neurons which are calculated using the Levenberg-Marquardt method.
- Feature selection may be applied on the input layer of the BPN (e.g., neural network 300 ) using regularization Bayesian methodology to reduce redundancy and to ensure better accuracy of the output.
- the final adjusted weights that minimize the error are mapped into the new input data to predict the new variables such as voltage and temperature.
- FIG. 4 shows a graph of exemplary input current data 126 a obtained from a battery system sensor 123 a .
- FIG. 5 shows a graph of exemplary predicted voltage data 272 a from training and testing the neural network using the input current data 126 a obtained from the battery system sensors 123 a shown in FIG. 4 .
- FIG. 6A shows a graph of exemplary predicted temperature data 272 b from training and testing the neural network using the input current data 126 a obtained from the battery system sensors 123 a shown in FIG. 4 .
- the controller 200 combines the inputs (e.g., current signal 126 a , voltage signal 126 b , or temperature signal 126 c ) from each of the sensors (e.g., the current sensor 123 a , the voltage sensor 123 b , or the temperature sensor 123 c , respectively) and applies the combined inputs as input features to train the neural network 300 .
- FIGS. 6B and 6C show a graph of exemplary predicted remaining capacity using the BPN.
- the predicted remaining capacity may correspond to training and testing the neural network 300 using the combined inputs, as previously described.
- the predicted remaining capacity may correspond to the predicted remaining capacity during a discharge mode of the energy storage system 120 determined based on a charge made of the energy storage system 120 .
- the controller 200 may track the remaining available capacity in the energy storage system 120 (e.g., the battery system 124 ) to determine a charge state or life cycle of the energy storage system 120 (e.g., the battery system 124 ).
- the controller 200 predicts future measurements by using some of the initial cycle's sensor data 126 .
- the controller estimates the parameters of the series based on the known AR model of order n.
- initial cycles are considered to calculate the parameters of the algorithm.
- the controller 200 may predict the data into the future.
- the empirical auto-regressive model is evolving in computing the parameters exposed in Equation (9) and Equation (10).
- the initial measurement Y 1 (voltage or temperature) is considered to be the average of the previous measurements.
- the slope and intercept are considered as well to be the average values of the previous slope and intercept. This method evolves over cycles and the parameters are updated to determine the new values.
- the Yule-Walker equations provide several routes to estimating the parameters of an AR(p) model, by replacing the theoretical covariances with estimated values.
- the Yule-Walker equations include computing the autocorrelation coefficients of the previous step measurements, and then applying those coefficients to predict the next measurements.
- the Yule-Walker set of equations in solving the AR(p) model are;
- [ ⁇ 1 ⁇ 2 ⁇ 2 ⁇ ⁇ p ] [ ⁇ 0 ⁇ - 1 ⁇ - 2 ... ... ... ⁇ 1 ⁇ 0 ⁇ - 1 ... ... ... ⁇ 2 ⁇ 1 ⁇ 0 ... ... ... ... ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ p - 1 ⁇ p - 2 ⁇ p - 3 ... ... ... ] ⁇ [ ⁇ 1 ⁇ 2 ⁇ 2 ⁇ ⁇ p ] ( 11 ⁇ a )
- the controller 200 may determine the ⁇ k and therefore predict X t .
- the standard deviation ⁇ ⁇ is equal to zero.
- FIG. 7 shows a graph of exemplary input current sensor data 126 a obtained by the battery system sensors 123 a .
- FIG. 8 shows a graph of exemplary predicted voltage data 272 a using the Yule-Walker approach; while FIG. 9 shows a graph of exemplary predicted temperature data 272 b using the Yule-Walker approach.
- the controller 200 may use one of three mathematical techniques to determine the predicted data/parameters 272 , i.e., predicted voltage data 272 a and predicted temperature data 272 b .
- Each one of the described mathematical techniques may output different predicted parameters 272 than the other mathematical techniques.
- the following discussion demonstrates the different performance metrics of the described mathematical techniques that the controller 200 may implement.
- Heat capacity C also known as thermal capacity, is a measurable physical quantity that is equal to the ratio of the heat added to (or removed from) an object to the resulting temperature change.
- FIG. 10 shows an overview of the heat exchange between the battery system 124 and its immediate environment.
- the battery system 124 for example, a battery string or bank that includes a number of cells/batteries that are connected in series to produce a battery or battery string with the usable voltage/potential
- n 1,2 (16a)
- T i mean ( T i min + T i max ) 2 ( 16 ⁇ b )
- K is a factor
- T mean is the mean temperature at temperature rise
- T ambient is the ambient temperature of the external environment of the battery system 124 .
- FIG. 11 illustrates a graph of an exemplary calculation of the maximum and minimum temperature associated with each cycle i; while FIG. 12 illustrates a graph of an exemplary prediction of the heat capacity and heat using thermodynamics equations and derivations of Equations (12-16b).
- Thermal Efficiency may be defined as the ratio of the heat utilized to the total heat produced electrically.
- the energy efficiency, also called thermal efficiency is a measure and defined as:
- the controller 200 may validate the predicted data 272 a , 272 b with data 126 collected from the sensors 123 using the Mean Square Error (MSE) equation:
- Frequency regulation is the injection and withdrawal of power on a second-by-second basis to maintain a threshold frequency. More specifically, an electric power grid transmits power from a power plant 110 to the end user using alternating current (AC), which oscillates at a specific frequency (e.g., 60 Hz for the Americas, and 50 Hz for Europe and Asia). A gap between power generation and usages causes the grid frequency to change. If demand is higher than supply, the frequency will fall, leading to brownouts and blackouts. If the power plants 110 generate more power than consumers 130 are using, the frequency goes up, potentially damaging the grid or the electric devices plugged into it.
- AC alternating current
- FIG. 13A illustrates a graph of an exemplary current frequency regulation profile
- FIG. 13B illustrates a graph of an exemplary voltage frequency regulation profile.
- High frequency oscillations of power distribution from the power plant are necessary to compensate for deviations in network voltage frequency due to high oscillations in total network load.
- the network 10 When applying high frequency power, in some examples, the network 10 experiences a drop in voltage. This drop in voltage often occurs during discharge. For example, when the energy storage system 120 reaches a minimum voltage (0% State of Charge), a power plant 110 is applied to the battery system 124 to quickly reach full-charge (100% State of Charge (SOC)) and allow the battery system 124 to function normally. Predicting this 0% SOC state is important to allowing the consumer 130 to anticipate the availability of the battery system 124 .
- SOC State of Charge
- the controller 200 uses a predefined fixed value to numerically define the voltage drop.
- the predefined fixed value may be equal to 350 V.
- the goal of the suggested algorithm is to predict all the voltages (i.e., voltage prediction data 272 a ) that manifest a value less than or equal to the fixed threshold (e.g., 350V).
- the results of the algorithm, as well as the measured data, are displayed in FIG. 14 .
- a visual inspection of FIG. 14 indicates that the predicted results (i.e., voltage prediction data 272 a ) greatly match the measured data.
- RMSE Root Mean Squared Error
- the RMSE is suggested to be equal to 1.26%, which leads to an accuracy of approximately 98.7%.
- the predictive algorithm described above with reference to FIGS. 1-14 may be beneficial in many aspects of managing and monitoring the battery system 124 when facing this type of use case.
- the predictive algorithm provides an accurate, fast, optimized method, based on accurate feature selections, to improve the accuracy of the predicted data 272 .
- the predictive algorithm may provide feedback that permits the BMS 122 to adjust the charge and discharge regime of the battery system 124 in real time.
- the predictive algorithm provides a time of the voltage drop and the magnitude of the voltage relative to a pre-defined power request.
- the controller 200 uses a scoring methodology based on a combination of multiple machine learning algorithms to predict the SOC of the battery system 124 .
- the controller 200 may use one of a fuzzy logic method, a support vector machine, or a deep neural network (e.g., neural network 300 ) to accurately predict the SOC of the battery system 124 .
- the controller 200 uses a scale methodology to scale the SOC between zero percent and one hundred percent to allow an operator to visualize and track the available power capacity of the battery system 124 and change the BMS system 122 .
- the cost to supply electricity varies during the course of a single day.
- the wholesale price of electricity on the power distribution network 100 reflects the real-time cost for supplying electricity from the power plant 110 .
- electricity demand is usually highest in the afternoon and early evening (peak hours), and costs to provide electricity are usually higher at these times.
- This use case considers that the battery system 124 is charged during low-price hours at the day-ahead spot market and then discharged during high-price hours. This use case is used to determine the performance of the energy storage system 120 when discharged at different levels of power for market bidding purposes.
- FIG. 15A An example of the power profile “Day Ahead Market” use case is provided in FIG. 15A .
- the battery system 124 is charged at constant power (e.g., about time 0-0.25), then discharged with different powers (e.g., about time greater than 2.25) to fulfill the high demand from the energy market. This operation is repeated for every battery of the battery system 124 .
- FIG. 15B illustrates a graph of the measured and the predicted voltages.
- the predictive data 272 perfectly fits the measured data.
- the RMSE is suggested to be equal to 0.026% for this example. As such, this leads to an accuracy approaching 100%.
- FIG. 16 illustrating two graphs A, B, the first graph A being a graph of the power/time of the battery system 124 and the second graph B being the SOC percentage/time of the battery system 124 , it is noticeable that the SOC % decreases due to high power discharge (e.g., about time greater than 2.5). This decrease is most apparent at 4 kW, where, the SOC drops to 60%.
- the prediction algorithms described above may also predict a minimum cut-off voltage. This corresponds to the lower voltage limit, which may be used to initiate a new charge regime allowing the battery system 124 to function normally, and therefore increase its life cycle.
- the prediction algorithms discussed above may provide important and beneficial measurements ahead of time.
- Some of the important and beneficial measurements may include the remaining time duration of the discharge at different powers depending on the market demand.
- Other important and beneficial measurements may include the voltage, the minimum cut-off voltage, and the SOC needed to monitor the discharge of the battery system 124 , while keeping the battery system 124 running smoothly.
- the distribution network 100 provides the consumer 130 with a complete array of the status information of the battery system 124 and system capabilities.
- the commercially relevant property of a battery installation is calculated from a minimal set of basis functions or basis variables in the time domain.
- energy efficiency is the most important property of the battery installation to be able to predict in the future since it directly drives the profitability of the installation and drive bidding strategies for the energy storage asset.
- the network 100 is configured to determine the forward projected energy efficiency of an energy storage installation from only the historical time dependent current and time dependent ambient temperature.
- the methods described are configured to determine a set of time invariant or slowly time varying parameters for each energy storage installation and use these to forward predict the performance given an assumed current and temperature profile.
- a biding software application may ask the BMS 122 what the efficiency would be for several different possible future load profiles and then choose the most profitable.
- FIG. 18 provides a schematic view of an exemplary arrangement of operations for a method 1800 of predicting future battery system parameters.
- the method 1800 includes receiving, at data processing hardware, current measurements from at least one current sensor configured to measure current of a battery system in communication with a power distribution network having a power plant distributing power to one or more consumers.
- the method 1800 includes receiving, at the data processing hardware, voltage measurements from at least one voltage sensor configured to measure voltage of the battery system.
- the method 1800 includes receiving, at the data processing hardware, temperature measurements from at least one temperature sensor configured to measure temperature of the battery system.
- the method 1800 includes determining, by the data processing hardware, an impedance parameter of the battery system based on the received measurements.
- the method 1800 includes determining, by the data processing hardware, a temperature parameter of the battery system based on the received measurements.
- the method 1800 includes determining, by the data processing hardware, a predicted voltage parameter based on the impedance parameter.
- the method 1800 includes determining, by the data processing hardware, a predicted temperature parameter based on the temperature parameter.
- the method 1800 includes commanding, by the data processing hardware, the battery system to charge (e.g., store) power from the power plant or discharge power from the power plant based on the predicted voltage parameter and the predicted temperature parameter.
- the method 1800 may include commanding, by the data processing hardware, the battery system to store power from the power plant or discharge power from the power plant based on the predicted voltage parameter and the predicted temperature parameter.
- FIG. 19 is a schematic view of an example computing device 1900 that may be used to implement the systems and methods described in this document.
- the computing device 1900 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers.
- the components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed in this document.
- the computing device 1900 includes a processor 1910 , memory 1920 , a storage device 1930 , a high-speed interface/controller 1940 connecting to the memory 1920 and high-speed expansion ports 1950 , and a low speed interface/controller 1960 connecting to low speed bus 1970 and storage device 1930 .
- Each of the components 1910 , 1920 , 1930 , 1940 , 1950 , and 1960 are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate.
- the processor 1910 can process instructions for execution within the computing device 1900 , including instructions stored in the memory 1920 or on the storage device 1930 to display graphical information for a graphical user interface (GUI) on an external input/output device, such as display 1980 coupled to high speed interface 1940 .
- GUI graphical user interface
- multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory.
- multiple computing devices 1900 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).
- the memory 1920 stores information non-transitorily within the computing device 1900 .
- the memory 1920 may be a computer-readable medium, a volatile memory unit(s), or non-volatile memory unit(s).
- the non-transitory memory 1920 may be physical devices used to store programs (e.g., sequences of instructions) or data (e.g., program state information) on a temporary or permanent basis for use by the computing device 1900 .
- Examples of non-volatile memory include, but are not limited to, flash memory and read-only memory (ROM)/programmable read-only memory (PROM)/erasable programmable read-only memory (EPROM)/electronically erasable programmable read-only memory (EEPROM) (e.g., typically used for firmware, such as boot programs).
- Examples of volatile memory include, but are not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), phase change memory (PCM) as well as disks or tapes.
- the storage device 1930 is capable of providing mass storage for the computing device 1900 .
- the storage device 1930 is a computer-readable medium.
- the storage device 1930 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations.
- a computer program product is tangibly embodied in an information carrier.
- the computer program product contains instructions that, when executed, perform one or more methods, such as those described above.
- the information carrier is a computer- or machine-readable medium, such as the memory 1920 , the storage device 1930 , or memory on processor 1910 .
- the high speed controller 1940 manages bandwidth-intensive operations for the computing device 1900 , while the low speed controller 1960 manages lower bandwidth-intensive operations. Such allocation of duties is exemplary only.
- the high-speed controller 1940 is coupled to the memory 1920 , the display 1980 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 1950 , which may accept various expansion cards (not shown).
- the low-speed controller 1960 is coupled to the storage device 1930 and low-speed expansion port 1970 .
- the low-speed expansion port 1970 which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet), may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
- input/output devices such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
- the computing device 1900 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 1900 a or multiple times in a group of such servers 1900 a , as a laptop computer 1900 b , or as part of a rack server system 1900 c.
- implementations of the systems and techniques described herein can be realized in digital electronic and/or optical circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof.
- ASICs application specific integrated circuits
- These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
- the computer programs, including the algorithms described herein are implemented in C++ using optimized and fast OPENMP classes and functions.
- the processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output.
- the processes and logic flows can also be performed by special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
- processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer.
- a processor will receive instructions and data from a read only memory or a random access memory or both.
- the essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data.
- a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.
- mass storage devices for storing data
- a computer need not have such devices.
- Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks.
- the processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
- one or more aspects of the disclosure can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen for displaying information to the user and optionally a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer.
- a display device e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen for displaying information to the user and optionally a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer.
- Other kinds of devices can be used to provide interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input
Abstract
Description
H(w)=f −T +T f(t)e −iωt dt,
where w=2πF, F is a frequency of a time series of the received measurements, defined as tϵRn and FϵC. The transfer function H(w) may be defined as a ratio between a Fourier transform of an output variable y(t) and an input variable x(t), where the output variable y(t) is one of the impedance parameter or the temperature parameter, and the input variable x(t) is one or more of the received measurements. The transfer function H(w) in a discrete domain may be determined as:
X t =c+Σ i=1 pφi X t-i+ϵi,
where φ1-φp are parameters of the model, c is a constant, and ϵt is white noise. The method may also include implementing a neural network approach, an empirical recursive method, or a Yule-Walker approach to determine an optimal solution of the auto-regressive model AR(p).
H(w)=∫−T +T f(t)e −iωt dt,
where w=2πF, F is a frequency of a time series of the received measurements, defined as tϵRn and FϵE Cn. The transfer function H(w) may be defined as a ratio between a Fourier transform of an output variable y(t) and an input variable x(t), where the output variable y(t) is one of the impedance parameter or the temperature parameter, and the input variable x(t) is one or more of the received measurements. The transfer function H(w) in a discrete domain may be determined as:
X t =c+Σ i=1 pφi X t-i+ϵi,
where φi-φp are parameters of the model, c is a constant, and ϵt is white noise. The method may also include implementing a neural network approach, an empirical recursive method, or a Yule-Walker approach to determine an optimal solution of the auto-regressive model AR(p).
H(w)=∫−T +T f(t)e −iωt dt,
where w=2πF, F is a frequency of a time series of the received measurements, defined as tϵRn and FϵCn. The transfer function H(w) may be defined as a ratio between a Fourier transform of an output variable y(t) and an input variable x(t), where the output variable y(t) is one of the impedance parameter or the temperature parameter, and the input variable x(t) is one or more of the received measurements. The transfer function H(w) in a discrete domain may be determined as:
H(w)=∫−T +T f(t)e −iωt dt (1)
where w=2πF, F is a frequency of the time series, tϵRn and FϵCn. H(w) is defined in a complex domain.
|H|=√{square root over (H real 2 +H imag2 2)}; (3)
where Hreal and Himag are the real and imaginary parts of the transfer function H(w).
TABLE 1 | ||||||
Predicted | Predicted | Predicted | Predicted | Predicted | ||
| Cycle | 1 | | Cycle3 | Cycle4 | Cycle5 |
Measured | 0.1460 | 0.0822 | 0.0717 | 0.0668 | 0.0224 | |
An accurate prediction of the remaining discharge time of the
X t =c+Σ i=1 pφi X t-i+ϵi; (5)
where φ1-φp are the parameters of the
F(x,w)=y; (6)
where x is the input vector presented to the network, w is the weight vector of the network, and y is the corresponding output vector approximated or predicted by the network. The weight vector w is commonly ordered first by layer, then by neurons, and finally by the weights of each neuron plus its bias.
(J T J+γ1)δ=J T E (7)
where J is the Jacobian matrix for the system, γ is the Levenberg's damping factor, δ is the weight update vector that the
where F(xi, w) the network function is evaluated for the ith input vector of the training set using the weight w and wj is the jth element of the weight vector w of the network.
wSum=Σi=1 nweighti*inputi.
The final adjusted weights that minimize the error are mapped into the new input data to predict the new variables such as voltage and temperature.
and where Yk is the kth time series, Y1 is the initial cycle time series, α and β are respectively the slope and the intercept. Y1 may represent the initial cycles up to ten cycles and may be updated in an online process to adjust its value.
X t=Σi=1 pφi X t-1+ϵi; (11b)
where the coefficients φi are calculated based on the following equation:
γ0=Σk=1 pφkγ−k+σϵ 2 (11c)
E=W+Q (12)
where E is the total change in internal energy of the system, i.e., the
ΔE=ΔW+ΔQ (13)
ΔW=∫ 0 t
Q=K·(T i mean ·T ambient)n |n=1,2 (16a)
Where
where K is a factor, Tmean is the mean temperature at temperature rise, and Tambient is the ambient temperature of the external environment of the
where C, ΔT, Ti mean, Tambient are the heat capacity, the difference between minimum and maximum temperatures at temperature rise, the mean temperature at temperature rise, and the ambient temperature, respectively.
where
Accuracy=(100−RMSE) % (20)
SOC(t)=SOC(t 0)+γ*∫P(t)charge *Δt dt+∫P(t)discharge *Δt dt (21)
where SOC(t) is the SOC at time t, P(t)charge is predicted power in a charge mode of the
Claims (18)
H(w)=∫−T +T∫(t)e −iωt dt,
X t =x+Σ i=1 pφf X t-i+ϵi;
H(w)=∫−T +T∫(t)e −iωt dt,
X t =c+Σ i=1 pφi X t-i+ϵi;
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